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Assessing the Readability of Covid-19 Testing Messages on the Internet
The COVID-19 pandemic first became evident at the end of 2019, and because of the many unknown aspects of this emerging infectious disease, the internet quickly became a source of information for consumers. It is important for any vital information to be written unambiguously, and at a level that ca...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910765/ https://www.ncbi.nlm.nih.gov/pubmed/33638806 http://dx.doi.org/10.1007/s10900-021-00973-6 |
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author | Garcia, Philip Fera, Joseph Mohlman, Jan Basch, Corey H. |
author_facet | Garcia, Philip Fera, Joseph Mohlman, Jan Basch, Corey H. |
author_sort | Garcia, Philip |
collection | PubMed |
description | The COVID-19 pandemic first became evident at the end of 2019, and because of the many unknown aspects of this emerging infectious disease, the internet quickly became a source of information for consumers. It is important for any vital information to be written unambiguously, and at a level that can be understood by all people regardless of education levels. The purpose of this study was to assess the readability of 50 sources of COVID19 testing information online. Only 6 websites out of 50 received an appropriate readability score on more than one assessment. One-sample, one-tailed t-tests (α = 0.05, df = 49) were used to see if the websites with information on COVID-19 testing are being written at appropriate reading levels. The resulting p-values indicate that each p-value recorded is substantially below 0.05, it is very unlikely that websites on this topic are being written at the recommended levels. Even the optimal messages on COVID-19 reflect a confusing and rapidly changing public health crisis, however if messages are kept simple and clear, individuals will have the best possible chance of optimizing behavioral mitigation strategies. These are compelling reasons for informational hosts to take necessary steps to ensure that messages are written in as simple terms as possible. To this end, it is suggested that internet sites dispersing COVID-19 testing information build in text analysis methods for all published messages, particularly those meant to inform best health practices in the time of a pandemic. |
format | Online Article Text |
id | pubmed-7910765 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer US |
record_format | MEDLINE/PubMed |
spelling | pubmed-79107652021-03-01 Assessing the Readability of Covid-19 Testing Messages on the Internet Garcia, Philip Fera, Joseph Mohlman, Jan Basch, Corey H. J Community Health Original Paper The COVID-19 pandemic first became evident at the end of 2019, and because of the many unknown aspects of this emerging infectious disease, the internet quickly became a source of information for consumers. It is important for any vital information to be written unambiguously, and at a level that can be understood by all people regardless of education levels. The purpose of this study was to assess the readability of 50 sources of COVID19 testing information online. Only 6 websites out of 50 received an appropriate readability score on more than one assessment. One-sample, one-tailed t-tests (α = 0.05, df = 49) were used to see if the websites with information on COVID-19 testing are being written at appropriate reading levels. The resulting p-values indicate that each p-value recorded is substantially below 0.05, it is very unlikely that websites on this topic are being written at the recommended levels. Even the optimal messages on COVID-19 reflect a confusing and rapidly changing public health crisis, however if messages are kept simple and clear, individuals will have the best possible chance of optimizing behavioral mitigation strategies. These are compelling reasons for informational hosts to take necessary steps to ensure that messages are written in as simple terms as possible. To this end, it is suggested that internet sites dispersing COVID-19 testing information build in text analysis methods for all published messages, particularly those meant to inform best health practices in the time of a pandemic. Springer US 2021-02-27 2021 /pmc/articles/PMC7910765/ /pubmed/33638806 http://dx.doi.org/10.1007/s10900-021-00973-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Paper Garcia, Philip Fera, Joseph Mohlman, Jan Basch, Corey H. Assessing the Readability of Covid-19 Testing Messages on the Internet |
title | Assessing the Readability of Covid-19 Testing Messages on the Internet |
title_full | Assessing the Readability of Covid-19 Testing Messages on the Internet |
title_fullStr | Assessing the Readability of Covid-19 Testing Messages on the Internet |
title_full_unstemmed | Assessing the Readability of Covid-19 Testing Messages on the Internet |
title_short | Assessing the Readability of Covid-19 Testing Messages on the Internet |
title_sort | assessing the readability of covid-19 testing messages on the internet |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7910765/ https://www.ncbi.nlm.nih.gov/pubmed/33638806 http://dx.doi.org/10.1007/s10900-021-00973-6 |
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